Two analysis examples on NFT and Helium
Thomas de Marchin
14DEC2022
Several Tb of data. Ethereum ~ 6x10^9 transactions.
Data are stored sequentially, requires developing specific tools to follow a transaction.
The structure of a transaction is difficult to read
Fragmentation of blockchain technologies
Two examples implemented in R (lots of visualisation packages):
OpenSea: big NFT market place
Weird Whales are managed by a specific smart contract on the Ethereum blockchain
To make it easier to extract information from the blockchain, we can read the events: dispatched signals (easy to read) the smart contracts can fire.
resEventTransfer <- GET("https://api.etherscan.io/api",
query = list(module = "logs",
action = "getLogs",
fromBlock = fromBlock,
toBlock = "latest",
address = "0x96ed81c7f4406eff359e27bff6325dc3c9e042bd",
topic0 = "0xddf252ad1be2c89b69c2b068fc378daa952ba7f163c4a11628f55a4df523b3ef",
apikey = EtherScanAPIToken)) Where is the sales price? On OpenSea, sales are managed by the main contract and if approved, the second contract is called (here Weird Whales), which then triggers the transfer \(\rightarrow\) need to download all the transactions from the OpenSea main smart contract address and then filter for the ones related to Weird Whales (~ 10000 API calls, can take several hours…).
Perfect data to be plotted as a network. We will display all the wallet addresses that have ever traded Weird Whales (restricted to tokens involved in at least 10 transactions).
Networks are described by:
network package:
About 2/3 of the transactions happened very shortly after the NFT’s creation